Spatial variability of soil fertility attributes and productivity in a coffee crop farm

Ferraz, Gabriel Araújo e Silva; Avelar, Raul; Bento, Nicole Lopes; Souza, Fábio Régis de; Ferraz, Patrícia Ferreira Ponciano; Damasceno, Flávio Alves; Barbari, Matteo

doi:10.15159/ar.19.142

Cited by 2 publications

(2 citation statements)

References 6 publications

Supporting

Mentioning

Contrasting

Order By: Relevance

“…The ranges (a) for pH ( Table 3 ) and DM ( Table 4 ) indicated that it was higher for soil pH. According to Ferraz et al [ 36 ], the range can be understood as the limiting distance at which a sampled point correlates with other points nearby. Therefore, range values can be used to define sample densities for future studies [ 42 ].…”

Section: Discussionmentioning

confidence: 99%

See 1 more Smart Citation

Mapping Soil and Pasture Attributes for Buffalo Management through Remote Sensing and Geostatistics in Amazon Biome

Valente

Ferraz

Santana

et al. 2022

Animals

View full text Add to dashboard Cite

The mapping of pastures can serve to increase productivity and reduce deforestation, especially in Amazon Biome regions. Therefore, in this study, we aimed to explore precision agriculture technologies for assessing the spatial variations of soil pH and biomass indicators (i.e., Dry Matter, DM; and Green Matter, GM). An experiment was conducted in an area cultivated with Panicum maximum (Jacq.) cv. Mombaça in a rotational grazing system for dairy buffaloes in the eastern Amazon. Biomass and soil samples were collected in a 10 m × 10 m grid, with a total of 196 georeferenced points. The data were analyzed by semivariogram and then mapped by Kriging interpolation. In addition, a variability analysis was performed, applying both the Normalized Difference Vegetation Index (NDVI) and Normalized Difference Water Index (NDWI) derived from satellite remote sensing data. The Kriging mapping between DM and pH at 0.30 m depth demonstrated the best correlation. The vegetative index mapping showed that the NDVI presented a better performance in pastures with DM production above 5.42 ton/ha−1. In contrast, DM and GM showed low correlations with the NDWI. The possibility of applying a variable rate within the paddocks was evidenced through geostatistical mapping of soil pH. With this study, we contribute to understanding the necessary premises for utilizing remote sensing data for pasture variable analysis.

show abstract

Section: Discussionmentioning

confidence: 99%

“…Analyses based on geostatistical techniques have also been used to determine spatial variability in the field [ 33 ], and are considered adequate for analyzing the spatial variability in the physical and chemical properties of soil and plants [ 34 , 35 , 36 ].…”

Section: Introductionmentioning

confidence: 99%